Comparative Life Cycle Assessment of Edible Vegetable Frying Oils
Bibliographic record
Abstract
Highlights Cottonseed oil (CSO), a cotton byproduct, has advantages for climate change compared to other seed oils. Results show that the cultivation phase is the main impact driver for all vegetable oils analyzed in this study. Refined CSO (U.S.) can reduce climate change impacts by up to 83% as compared to the other oils analyzed. Abstract. Edible vegetable oils are a major source of climate change impacts and an environmental concern in the processed food industry. This study consists of a cradle-to-grave life cycle assessment (LCA) of refined U.S. cottonseed oil (CSO), global soybean oil, U.S. canola oil, and palm oil sourced from Indonesia and Malaysia. Considering the oils equivalent for deep frying, they are compared on a 1 kg of oil basis. Analysis includes sensitivity analyses for modeling allocation choices and oil mixes as well as uncertainty analysis. Results show that the cultivation phase is the main impact driver for all vegetable oils analyzed, which favors CSO (U.S.) because it is a co-product. Refined CSO (U.S.) can reduce climate change impacts by up to 83%. Overall, refined CSO (U.S.) was a top performer in six of the eight impact categories evaluated. When ranking the oils, refined CSO (U.S.) was the preferred choice. Despite being the preferred choice, there are tradeoffs with CSO, such as water scarcity. In the context of global-scale commercial frying applications, e.g., McDonald’s daily French fry production of 9 million tons per day, switching the frying oil to refined CSO (U.S.) represents potential savings of 1,130 to 2,188 tons of CO2-eq d-1. For fast-food chains seeking to reduce their climate change impacts, refined CSO (U.S.) may be useful in frying applications. However, opportunities may exist for improvement in water use efficiency in the cultivation phase, which reinforces the need for continuous improvements in agriculture. Keywords: Comparative life cycle assessment, Canola oil, Cottonseed oil, Cotton sustainability, Fast-food industry, LCA, Palm oil, Soybean oil, Vegetable frying oils.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".